Khushiyant Chauhan
Senior MLE at Airmason
Freiburg, Germany
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I'm a Machine Learning Engineer and Data Scientist with 6+ years of experience building scalable ML solutions across startups from pre-seed to Series D, and research institutions like NTNU and CERN. I specialize in designing end-to-end ML systems with expertise in MLOps workflows, model deployment, and production-grade infrastructure
My work spans diverse domains—from 3D generative modeling pipelines in manufacturing to lightweight LLM finetuning for task-specific NLP systems. I’ve also developed robust data collection pipelines, automated benchmarking tools, and contributed to compiler research at CERN aimed at optimizing ML plugin infrastructure. I thrive at the intersection of engineering and research, with published work in neurocognitive modeling and efficient deep learning. Whether working with distributed teams or on open-source R&D.
Area of Expertise
Topics
Clear Skies Ahead: Navigating Cloud Monitoring
Have you ever attended a Cloud monitoring presentation and walked away with more questions than answers? This session will finally provide practical guidance on how it actually cloud system monitoring works and what are its requirements.We’ll cover what you’ll need to build out your own cloud infrastructure and monitor its resources, security etc.
Some of the topics that we'll explore are:
- Deploying dummy services (for further hands-on practice)
- How attackers exploit poor infrastructure
- Objectives of Cloud monitoring
- What to monitor such as Error rate, latency, reports, traffic
- Optimising Cost
- Use of various tech such as k8s, docker, datadog
This will benefit both newcomers and seasoned professionals of the DevOps field by making them learn and remember the cloud monitoring importance respectively.
Role of DevOps in deployment and maintenance of ML models
In this speaker session, we will explore the role of DevOps in the deployment and maintenance of machine learning models. We will discuss how DevOps practices such as continuous integration, continuous deployment, and infrastructure as code can be applied to the ML workflow to improve collaboration, efficiency, and scalability. Attendees will learn about tools and techniques for automating the deployment and monitoring of ML models, as well as strategies for managing and updating models in production. The session will also cover best practices for testing, debugging, and troubleshooting ML models, with a focus on maintaining high accuracy and performance in production environments. By the end of the session, attendees will have a better understanding of how to leverage DevOps to optimize the deployment and maintenance of ML models.
Session will likely consists of the following segments:
- Explanation of how DevOps practices can improve collaboration, efficiency and scalability in ML model deployment and maintenance
- Discussion on the importance of version control and configuration management in ML models
Understanding of the role of containers and container orchestration in ML model deployment
- Explanation of how to implement Continuous integration and Continuous Deployment for ML models
- Overview of monitoring and logging practices specific to ML models
- Strategies for A/B testing and rollbacks in ML model deployment
- Best practices for maintaining model performance in production
- Understanding of how to manage model drift and retraining in production environments
- Discussions on how to handle data privacy and security in ML model deployment and maintenance.
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